Lampiran 1 : Data Pemeliharaan Alat Berat KEY PERFORMANCE INDICATOR ( KPI ) Periode EQUIP. NO
Jan-08 MODEL
DOWN TIME I
II
III
IV
V
TOTAL DOWN TIME
FREQ OF DT
SHM TOTAL
1
A11DT01
773B/D
0
36
24
1
0
61
4
550
2
A11DT02
773B/D
126
126
126
39
0
417
5
238
3
A11DT03
773B/D
1
1
0
0
3
5
3
254
4
A11DT04
773B/D
20
8
3
8
0
39
5
424
5
A11DT05
769C
2
2
0
10
12
26
6
180
6
A11DT06
769C
0
0
0
0
0
0
0
119
7
A11DT07
769C
7
0
0
0
1
8
2
223
8
A11DT08
769C
0
0
0
0
0
0
0
244
9
A11DT10
773B/D
126
126
126
126
72
576
5
94
10 A11DT11
773B/D
30
84
1
1
8
124
7
619
11 A11DT12
773B/D
0
1
0
1
12
14
3
716
12 A11DT13
777D
0
0
2
28
0
30
3
479
13 A11DT14
777D
0
12
1
0
2
15
3
230
14 A11DT15
777D
0
108
0
3
2
113
5
508
15 A11DT16
777D
0
4
0
2
15
21
3
463
Penerapan model..., Deni Juharsyah, FT UI, 2009.
Lampiran 1 : Data Pemeliharaan Alat Berat (Lanjutan)
KEY PERFORMANCE INDICATOR ( KPI ) Periode EQUIP. NO
: Februari 2008 MODEL
DOWN TIME I
II
III
IV
V
TOTAL FREQ OF DOWN TIME DT
SHM TOTAL
A11DT01
773B/D
0
0
0
0
0
0
0
186
A11DT02
773B/D
0
0
0
0
0
0
0
132
A11DT03
773B/D
0
0
0
1
1
2
2
128
A11DT04
773B/D
1
12
1
8
2
24
6
184
A11DT05
769C
0
82
44
1
0
127
4
51
A11DT06
769C
0
0
0
3
0
3
1
59
A11DT07
769C
0
3
0
0
0
3
2
88
A11DT08
769C
0
0
0
0
1
1
1
133
A11DT10
773B/D
54
126
126
21
13
340
6
108
A11DT11
773B/D
1
0
0
11
5
17
6
278
A11DT12
773B/D
0
0
0
3
35
38
4
314
A11DT13
777D
0
0
2
28
2
32
3
197
A11DT14
777D
0
0
1
0
0
1
1
69
A11DT15
777D
0
0
0
0
0
0
0
246
A11DT16
777D
0
0
0
0
0
0
0
198
Penerapan model..., Deni Juharsyah, FT UI, 2009.
Lampiran 1 : Data Pemeliharaan Alat Berat (Lanjutan)
KEY PERFORMANCE INDICATOR ( KPI ) Periode EQUIP. NO
: Maret 2008 MODEL
DOWN TIME I
II
III
IV
V
VI
TOTAL FREQ OF DOWN TIME DT
SHM TOTAL
1
A11DT01
773B/D
0
4
2
2
0
0
8
6
294
2
A11DT02
773B/D
0
0
0
0
0
0
0
0
0
3
A11DT03
773B/D
0
0
2
12
98
18
130
4
186
4
A11DT04
773B/D
0
16
2
102
37
0
157
6
174
5
A11DT05
769C
14
0
2
62
40
0
118
4
100
6
A11DT06
769C
0
0
0
0
0
0
0
0
0
7
A11DT07
769C
0
0
1
0
0
0
1
1
187
8
A11DT08
769C
0
0
0
0
0
0
0
0
206
9
A11DT10
773B/D
0
51
1
7
1
0
60
5
382
10
A11DT11
773B/D
0
1
9
1
1
0
12
4
443
11
A11DT12
773B/D
0
12
0
9
1
18
40
5
425
12
A11DT13
777D
0
19
3
6
80
18
126
5
296
13
A11DT14
777D
0
2
0
44
48
0
94
3
154
14
A11DT15
777D
0
0
0
4
0
0
4
1
441
15
A11DT16
777D
0
102
62
2
0
0
166
3
274
Penerapan model..., Deni Juharsyah, FT UI, 2009.
Lampiran 1 : Data Pemeliharaan Alat Berat (Lanjutan) KEY PERFORMANCE INDICATOR ( KPI ) Periode
: April DOWN TIME
TOTAL FREQ OF DOWN TIME DT
SHM TOTAL
EQUIP. NO
MODEL I
II
III
IV
V
1
A11DT01
773B/D
65
126
126
126
54
497
6
37
2
A11DT02
773B/D
0
0
7
2
0
9
3
274
3
A11DT03
773B/D
94
0
0
0
0
94
2
137
4
A11DT04
773B/D
1
1
2
7
30
41
7
268
5
A11DT05
769C
0
0
0
116
36
152
2
143
6
A11DT06
769C
0
0
0
0
0
0
0
45
7
A11DT07
769C
2
0
0
2
2
6
3
255
8
A11DT08
769C
0
0
90
100
0
190
2
172
9
A11DT10
773B/D
0
0
1
72
0
73
2
351
10
A11DT11
773B/D
0
0
0
0
0
0
0
397
11
A11DT12
773B/D
108
126
54
0
0
288
3
197
12
A11DT13
777D
30
0
0
0
2
32
2
315
13
A11DT14
777D
0
12
0
0
0
12
1
138
14
A11DT15
777D
0
1
0
0
0
1
1
362
15
A11DT16
777D
12
32
26
0
2
72
4
291
Penerapan model..., Deni Juharsyah, FT UI, 2009.
Lampiran 1 : Data Pemeliharaan Alat Berat (Lanjutan) KEY PERFORMANCE INDICATOR ( KPI ) Periode EQUIP. NO
: Mei MODEL
DOWN TIME I
II
III
IV
V
TOTAL FREQ OF DOWN TIME DT
SHM TOTAL
1
A11DT01
773B/D
0
0
0
0
0
0
0
0
2
A11DT02
773B/D
0
24
0
0
0
24
1
409
3
A11DT03
773B/D
0
0
0
0
0
0
0
245
4
A11DT04
773B/D
72
0
20
4
40
136
9
220
5
A11DT05
769C
0
0
0
0
0
0
0
183
6
A11DT06
769C
0
3
0
0
0
3
1
88
7
A11DT07
769C
48
26
0
0
0
74
3
173
8
A11DT08
769C
0
0
0
0
0
0
0
193
9
A11DT10
773B/D
0
25
11
18
0
54
4
200
10
A11DT11
773B/D
0
0
2
2
9
13
4
409
11
A11DT12
773B/D
0
24
5
10
10
49
7
351
12
A11DT13
777D
0
5
0
0
2
7
3
407
13
A11DT14
777D
0
0
0
0
0
0
0
0
14
A11DT15
777D
0
10
0
6
1
17
3
429
15
A11DT16
777D
0
0
0
2
0
2
1
426
Penerapan model..., Deni Juharsyah, FT UI, 2009.
Lampiran 1 : Data Pemeliharaan Alat Berat (Lanjutan)
KEY PERFORMANCE INDICATOR ( KPI ) Periode EQUIP. NO
: June MODEL
TOTAL DOWN FREQ OF DT TIME
DOWN TIME I
II
III
IV
SHM TOTAL
A11DT01
773B/D
0
0
0
5
5
2
31
A11DT02
773B/D
0
0
0
1
1
1
327
A11DT03
773B/D
21
102
3
36
162
5
118
A11DT04
773B/D
0
0
12
126
138
2
242
A11DT05
769C
0
0
0
126
126
1
119
A11DT06
769C
0
0
1
0
1
1
104
A11DT07
769C
0
0
0
0
0
0
124
A11DT08
769C
0
0
0
0
0
0
129
A11DT10
773B/D
0
0
0
0
0
0
0
A11DT11
773B/D
5
12
0
7
24
4
307
A11DT12
773B/D
0
0
3
0
3
1
305
A11DT13
777D
2
9
30
0
41
4
103
A11DT14
777D
0
0
0
0
0
0
71
A11DT15
777D
2
1
0
0
3
2
291
A11DT16
777D
3
3
10
0
16
3
296
Penerapan model..., Deni Juharsyah, FT UI, 2009.
Lampiran 1 : Data Pemeliharaan Alat Berat (Lanjutan) KEY PERFORMANCE INDICATOR ( KPI ) Periode
: July 2008
EQUIP. NO
MODEL
TOTAL DOWN FREQ OF DT TIME
DOWN TIME I
II
III
IV
V
SHM TOTAL
1
A11DT01
773B/D
0
5
9
36
54
104
6
526
2
A11DT02
773B/D
5
0
6
0
0
11
2
619
3
A11DT03
773B/D
0
2
4
8
0
14
4
616
4
A11DT04
773B/D
126
126
90
8
0
350
3
280
5
A11DT05
769C
126
126
94
0
0
346
1
284
6
A11DT06
769C
0
0
0
0
9
9
1
621
7
A11DT07
769C
0
0
0
0
0
0
0
630
8
A11DT08
769C
0
0
7
13
0
20
2
610
9
A11DT10
773B/D
90
0
0
0
0
90
1
540
10
A11DT11
773B/D
7
8
2
0
28
45
5
585
11
A11DT12
773B/D
0
3
9
102
72
186
4
444
12
A11DT13
777D
90
0
0
0
0
90
1
540
13
A11DT14
777D
0
3
2
0
3
8
3
622
14
A11DT15
777D
0
17
3
0
0
20
3
610
15
A11DT16
777D
4
5
0
0
0
9
2
621
KEY PERFORMANCE INDICATOR ( KPI ) Periode EQUIP. NO
: Agustus 2008 TOTAL DOWN FREQ OF DT TIME
DOWN TIME
MODEL I
II
III
IV
V
SHM TOTAL
1
A11DT01
773B
54
126
126
126
37
469
1
33
2
A11DT02
773B
0
4
0
13
0
17
3
394
3
A11DT03
773B
0
120
18
0
0
138
1
95
4
A11DT04
773B
2
2
39
4
0
47
4
230
5
A11DT05
769C
0
0
0
3
0
3
1
125
6
A11DT06
769C
0
0
4
0
44
48
2
134
7
A11DT07
769C
0
0
18
2
0
20
3
263
8
A11DT08
769C
0
6
0
9
14
29
5
214
9
A11DT10
773D
1
0
0
3
3
7
3
381
10
A11DT11
773D
12
0
5
10
2
29
6
371
11
A11DT12
773D
2
0
0
0
14
16
3
441
12
A11DT13
777D
0
0
0
8
0
8
1
141
13
A11DT14
777D
22
0
0
0
3
25
2
340
14
A11DT15
777D
0
4
1
0
18
23
5
324
15
A11DT16
777D
2
2
2
0
0
6
3
341
Penerapan model..., Deni Juharsyah, FT UI, 2009.
Lampiran 1 : Data Pemeliharaan Alat Berat (Lanjutan) KEY PERFORMANCE INDICATOR ( KPI ) Periode
: September 2008 TOTAL DOWN FREQ OF DT TIME
DOWN TIME
EQUIP. NO
MODEL I
II
III
IV
1
A11DT01
773B
10
5
24
0
0
39
6
39
2
A11DT02
773B
2
20
16
1
30
69
8
69
3
A11DT03
773B
0
0
50
1
0
51
3
51
4
A11DT04
773B
0
2
12
32
4
50
10
50
5
A11DT05
769C
12
0
0
7
0
19
2
19
6
A11DT06
769C
0
0.5
5
0
0
5.5
4
6
7
A11DT07
769C
6
0
1
2
0
9
5
9
8
A11DT08
769C
0
23
44
0
2
69
3
69
9
A11DT10
773D
10
7
0
4
0
21
6
21
SHM TOTAL
V
10
A11DT11
773D
0
0
2
0
4
6
2
6
11
A11DT12
773D
4
116
126
126
18
390
3
390
12
A11DT13
777D
8
2
8
1
1
20
8
20
13
A11DT14
777D
2
0
13
0
0
15
3
15
14
A11DT15
777D
4
0
3
6
0
13
5
13
15
A11DT16
777D
8
12
12
6
2
40
6
40
KEY PERFORMANCE INDICATOR ( KPI ) Periode EQUIP. NO
: Oktober MODEL
DOWN TIME I
II
III
IV
TOTAL DOWN TIME
FREQ OF DT
SHM TOTAL
V
1
A11DT01
773B
1
9
0
10
4
24
5
24
2
A11DT02
773B
90
14
3
2
48
157
4
157
3
A11DT03
773B
40
126
72
48
18
304
3
304
4
A11DT04
773B
0
1
1
0
10
12
4
12
5
A11DT05
769C
0
1
9
0
4
14
4
14
6
A11DT06
769C
0
2
0
0
5
7
3
7
7
A11DT07
769C
0
2
0
0
0
2
1
2
8
A11DT08
769C
10
58
0
0
0
68
4
68
9
A11DT10
773D
0
2
0
10
40
52
4
52
10
A11DT11
773D
2
4
10
4
0
20
5
20
11
A11DT12
773D
2
0
1
8
2
13
4
13
12
A11DT13
777D
3
0
0
0
11
14
3
14
13
A11DT14
777D
0
1
3
0
0
4
2
4
14
A11DT15
777D
0
10
15
0
1
26
4
26
15
A11DT16
777D
0
2
90
126
90
308
2
308
Penerapan model..., Deni Juharsyah, FT UI, 2009.
Lampiran 1 : Data Pemeliharaan Alat Berat (Lanjutan) KEY PERFORMANCE INDICATOR ( KPI ) Periode
: November
EQUIP. NO
TOTAL DOWN FREQ OF DT TIME
DOWN TIME
MODEL I
II
III
IV
V
18
9
36
71
SHM TOTAL
1
A11DT01
773B
0
8
7
71
2
A11DT02
773B
2
0
4
2
1
9
5
9
3
A11DT03
773B
0
0
64
1
0
65
2
65
4
A11DT04
773B
2
8
14
126
126
276
5
276
5
A11DT05
769C
0
0
12
3
0
15
3
15
6
A11DT06
769C
0
0
0
0
0
0
0
0
7
A11DT07
769C
0
1
4
0
0
5
2
5
8
A11DT08
769C
0
0
0
8
0
8
1
8
9
A11DT10
773D
22
102
97
50
0
271
3
271
10
A11DT11
773D
0
3
20
14
4
41
5
41
11
A11DT12
773D
0
2
0
0
8
10
2
10
12
A11DT13
777D
1
4
10
0
6
21
6
21
13
A11DT14
777D
0
0
0
0
0
0
0
0
14
A11DT15
777D
0
9
8
1
0
18
4
18
15
A11DT16
777D
36
126
126
126
126
540
2
540
KEY PERFORMANCE INDICATOR ( KPI ) Periode EQUIP. NO
: December TOTAL DOWN FREQ OF DT TIME
DOWN TIME
MODEL I
II
III
IV
SHM TOTAL
1
A11DT01
773B
30
0
0
4
34
2
34
2
A11DT02
773B
0
8
0
0
8
1
8
3
A11DT03
773B
12
0
22
10
44
4
44
4
A11DT04
773B
126
126
126
126
504
0
504
5
A11DT05
769C
0
0
0
0
0
0
0
6
A11DT06
769C
0
80
0
0
80
2
80
7
A11DT07
769C
0
0
0
2
2
1
2
8
A11DT08
769C
5
2
44
72
123
3
123
9
A11DT10
773D
0
0
8
8
16
2
16
10
A11DT11
773D
4
6
0
0
10
2
10
11
A11DT12
773D
29
0
0
7
36
4
36
12
A11DT13
777D
0
0
0
12
12
1
12
13
A11DT14
777D
0
4
0
0
4
1
4
14
A11DT15
777D
0
0
5
0
5
1
5
15
A11DT16
777D
126
126
126
126
504
0
504
Penerapan model..., Deni Juharsyah, FT UI, 2009.
Lampiran 2 : Kuesioner Tingkat Kepentingan DATA RESPONDEN
1. Nama:
2. Umur:
3. Pendidikan Formal Terakhir:
4. Pengalaman Kerja (dalam tahun):
5.
Jenis alat berat yang dioperasikan (truck/loader/grader/dozer):
6. No unit alat berat yang dioperasikan :
Citeureup, …...April 2009 Tanda Tangan Responden
(
Penerapan model..., Deni Juharsyah, FT UI, 2009.
)
Lampiran 2 : Kuesioner Tingkat Kepentingan (Lanjutan)
PETUNJUK PENGISIAN KUESIONER Berilah nilai skor yang paling Bapak anggap penting terkait dengan pemeliharaan alat berat yang dilaksanakan di workshop berdasarkan skala berikut ini:
5 = Kondisi yang ada dianggap Sangat Penting. 4 = Kondisi yang ada dianggap Penting. 3 = Kondisi yang ada dianggap Sedang. 2 = Kondisi yang ada dianggap Kurang Penting. 1 = Kondisi yang ada dianggap Tidak Penting. CONTOH PENGISIAN KUESIONER PENILAIAN TERHADAP TINGKAT No.
KEPENTINGAN PEMELIHARAAN ALAT BERAT
1 2 3
Kondisi tempat duduk operator Kenyamanan berkendara Respons dari teknisi terhadap laporan kerusakan
Nilai Skor: 5 = Sangat Penting 4 = Penting 3 = Sedang 2 = Kurang Penting 1 = Tidak Penting 5 4 3 2 1 5 4 3 2 1 5 4 3 2 1
Kondisi tempat duduk operator terkait dengan kualitas pemeliharaannya. dianggap Sangat Penting Kenyamanan berkendara terkait dengan kualitas pemeliharaannya dianggap Penting . Respons dari teknisi terhadap laporan kerusakan dari operator dianggap Tidak Penting.
Penerapan model..., Deni Juharsyah, FT UI, 2009.
Lampiran 2 : Kuesioner Tingkat Kepentingan (Lanjutan)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Kondisi tempat duduk operator Kondisi cermin / mirror Kondisi lampu Kondisi AC Kondisi panel-panel di kabin operator Kondisi gauge di kabin operator Kondisi engine Kondisi transmisi Kondisi suspensi Kondisi komponen hydraulic Kondisi final drive Kondisi ban Kondisi steering Kondisi rem Kondisi klakson Kondisi wiper Kondisi emergency shutdown switch Kondisi Alat Pemadam Api Ringan (APAR) Penangan terhadap bolt / nut yang kendur Penanganan terhadap adanya oli yang bocor
Nilai Skor: 5 = Sangat Penting 4 = Penting 3 = Sedang 2 = Kurang Penting 1 = Tidak Penting 5 4 3 2 1 5 4 3 2 1 5 4 3 2 1 5 4 3 2 1 5 4 3 2 1 5 4 3 2 1 5 4 3 2 1 5 4 3 2 1 5 4 3 2 1 5 4 3 2 1 5 4 3 2 1 5 4 3 2 1 5 4 3 2 1 5 4 3 2 1 5 4 3 2 1 5 4 3 2 1 5 4 3 2 1 5 4 3 2 1 5 4 3 2 1 5 4 3 2 1
21 22 23 24 25
Kenyamanan saat berkendara Penggunaan bahan bakar Service (PM) secara teratur Respon teknisi terhadap laporan kerusakan Tingkat keahlian teknisi
5 5 5 5 5
4 4 4 4 4
3 3 3 3 3
2 2 2 2 2
1 1 1 1 1
26
Penanggulangan terhadap kerusakan yang sering terjadi / berulang - ulang.
5
4
3
2
1
5 5 5 5 5
4 4 4 4 4
3 3 3 3 3
2 2 2 2 2
1 1 1 1 1
PENILAIAN TERHADAP TINGKAT No.
KEPENTINGAN PEMELIHARAAN ALAT BERAT
27 28 29 30 31
Lainnya……….
Penerapan model..., Deni Juharsyah, FT UI, 2009.
Lampiran 3 : Kuesioner Tingkat Kepentingan dan Kepuasan Tingkat Kepuasan No.
Tingkat Kepentingan
Atribut Keinginan Pelanggan TB
KB
CB
B
SB
TP
1 Kondisi tempat duduk operator 2 Kondisi cermin / mirror 3 Kondisi lampu 4 Kondisi AC 5 Kondisi panel-panel di kabin operator 6 Kondisi gauge di kabin operator 7 Kondisi engine 8 Kondisi transmisi 9 Kondisi suspensi 10 Kondisi komponen hydraulic 11 Kondisi final drive 12 Kondisi ban 13 Kondisi steering 14 Kondisi rem 15 Kondisi klakson 16 Kondisi wiper 17 Kondisi emergency shutdown switch 18 Kondisi Alat Pemadam Api Ringan (APAR) 19 Penangan terhadap bolt / nut yang kendur 20
Penanganan terhadap adanya oli yang bocor
21 Kenyamanan saat berkendara 22 Penggunaan bahan bakar 23 Service (PM) secara teratur 24 Respon teknisi terhadap laporan kerusakan 25 Tingkat keahlian teknisi 26
Penanggulangan terhadap kerusakan yang sering terjadi / berulang - ulang.
Penerapan model..., Deni Juharsyah, FT UI, 2009.
KP
CP
P
SP
Lampiran 4 : House of Quality (HOQ)
Penerapan model..., Deni Juharsyah, FT UI, 2009.
The Implementation of Maintenance Quality Function Deployment (MQFD) for Improving Maintenance Quality at Mining Industry M. Dachyar1, Erlinda Muslim2 dan Deni Juharsyah3 Industrial Engineering Department, Universitas of Indonesia, Depok Email:
[email protected],
[email protected],
[email protected] Abstract The heavy equipment has an important role to support the mining industry activity. The heavy equipment will operating well if maintained properly. Good maintenance relate with the election of the maintenance strategy. Maintenance Quality Function Deployment (MQFD) is a model that introduced by Pramod et. al. to improve maintenance quality through the strategic decision development. The strategic decision developed based on the voice of customer, eight pillars of Total Productive Maintenance (TPM) and the maintenance parameters on TPM. The voice of customer is gathered by spreading the survey and used to determine the priority of the maintenance quality aspect. The prioritized voice of customer then translated into technical language which will be implemented by the workshop to improve the maintenance quality based on eight pillars of TPM. Both of maintenance quality aspect and technical language are generated by the development of House of Quality (HOQ) that usually used in Quality Function Deployment (QFD) method. The TPM’s maintenance parameters used as an indicator to measure the performance of the strategy implementation. The indicator make the MQFD model has the ability to develop the maintenance quality continuous improvement. Key Words : Heavy equipment, maintenance strategy, Maintenance Quality Function Deployment (MQFD), Total Productive Maintenance (TPM), House of Quality (HOQ)
1.
Introduction
At mining industry, maintenance is an important issue. It’s due to the majority of mining industry activities using mechanical devices to support it, so that the production activity depend on the availability of the mechanical devices. One of the important mechanical device at the mining industry is the heavy equipment. Most of the activity at mining industry using the heavy equipment. To guarantee the availability of the heavy equipment, good maintenance strategy is a must. But it’s not an easy matter to have good maintenance practise at mining industry due to its high utilization & mobilitation. Beside that, heavy equipment has high sensitivity to operational abuse so that the operator skill has big influence to determine the heavy equipment condition. That’s why the responsibility to the heavy equipment health not only on the maintenance crew, but also on the operator that using the heavy equipment. By the developing of the industrial world, the organizations choosing to focus on its core
business and outsourcing another area outscope their core business. It’s also happened at the mining industry. This condition force the company to have good communication and coorperation between all organization involved on their business. The implementation of Maintenance Quality Function Deployment (MQFD) model at the mining industry expected can improve the quality of maintenance and also the coorperation and communication between the maintenance crew and the heavy equipment operator through the existing customer voice. The objective of this research is to get the maintenance strategy that can improve the maintenance quality and the productivity of heavy equipment based on voice of customer by implementing the Maintenance Quality Function Deployment (MQFD) model. 2.
Basic Theory
The MQFD model was introduced at the first time by Pramod, Devadasan, Muthu, Jagathyraj & Moorthy on 2006 through a journal
Penerapan model..., Deni Juharsyah, FT UI, 2009.
“Integrating TPM and QFD for improving quality in maintenance engineering”. The MQFD model is an integrating method of QFD and TPM. The integration of these two method expected can improve the maintenance quality and also accomadate the VOC both of internal and external customer comparing the existing maintenance method. Figure 1 is a MQFD model that introduced by Pramod et.al. From the MQFD model at figure 1, the company performance can be known from the customer voice. The customer voice is used to develop the house of quality (HOQ). The result of QFD is the technical language that will be delivered to top management to make the strategic decision. The technical languages which are concerned with enhancing maintenance quality are strategically directed by the top management for progressing through the eight TPM pillars. The TPM characteristics developed through the development of eight pillars are fed into the production system. This implementation shall be focussed on the increasing of the maintenance quality parameters’ values that are availability, Mean Time To Repair (MTTR), Mean Time Between Failure (MTBF), Mean Down Time (MDT) dan Overall Equipment Effectiveness (OEE). The results of this implementation then used to develop another HOQ by comparing it with the decided target. This process will form the new cycle of MQFD model.
3.
Data Collection and Calculation
The data was collected at a mining company on Bogor, West Java. The data consist of production data, heavy equipment maintenance history and the respondent’s satisfaction level of maintenance quality at workshop. The maintenance history data calculated based on the maintenance parameters of Total Productive Maintenance (TPM). Availabilty is a measure of what percentage of the total time the heavy equipment is available for used. Availabilty (A) calculated using the formula: A =
ScheduledR unningTime − Downtime x100 % ScheduledR unningTime
Mean Down Time (MDT) is the average down time of the heavy equipment. MDT calculated using the formula: TotalDownt ime
MDT = FrekuensiD ownTime Mean Time Beetween Failures (MTBF) is the average time a heavy equipment would run trouble-free before experiencing any sort of failure. MTBF calculated using the formula: MTBF =
TimeBetweenFailure NumberofFailure
Mean Time To Repair (MTTR) is the average time taken to repair once it is brought into service.
Source : Journal of Quality in Maintenance Engineering,Vol. 13 No.4, 2007, p. 340 – 343
Figure 1. MQFD Model
Penerapan model..., Deni Juharsyah, FT UI, 2009.
MTTR calculated using the formula: Total Re pairTime MTTR = Numberof Re pair
At the workshop which this research took place, Mean Time To Repair (MTTR) equal to Mean Down Time (MDT). OEE is the important parameter to measure the success of TPM implementation. To get OEE, it’s need to calculate the Availability (A), Performance Efficiency (P) and Rate of Quality (Q) first. OEE calculated using the formula: OEE = A x P x Q where : ScheduledR unningTime − Downtime x100% A= ScheduledR unningTime
P= Q= 4.
Pr ocessedAmount x100% OperatingTime / TheoriticalCycleTime
Pr ocessedAmo unt − DefectAmou nt x100% Pr ocessedAmo unt
The Result of Research
Maintenance Quality Function Deployment (MQFD) model consist of two big step of design. The design of HOQ started by determining the priority of the attributes. The maintenance quality attributes were obtained from the direct interviewing of workshop superintendent and dealer maintenance supervisor. The determining of attribute priority calculated based on the weighting of 15 operators’ assessment to the maintenance quality aspects on the workshop. The total score is obtained from the answer of each maintenance quality aspects that calculated using the formula: Total Score = (N1 x 5) + (N2 x 4) + (N3 x 3) + (N4 x 2) + (N5 x 1) where : N1 = Number of ”not good ” answer N2 = Number of “little not good ” N3 = Number of ”fair” N4 = Number of ”good ” N5 = Number of ”very good ” As a sample, the priority score of ”the operator seat condition” is:
The operator seat condition = (0x5) + (6x4) + (3x3) + (5x2) + (1x1) = 44. With the same way, it can be determined the priority score for the other aspects which can be seen in table 1. Table 1. The Priority Score of Maintenance Aspects Score
Urutan Prioritas
1 Kondisi tempat duduk operator
44
4
2 Kondisi cermin / mirror
45
3
3 Kondisi lampu
41
7
4 Kondisi AC
42
6
5 Kondisi panel-panel di kabin operator
47
2
6 Kondisi gauge di kabin operator
41
7
7 Kondisi engine
33
13
8 Kondisi transmisi
37
10
9 Kondisi suspensi
43
5
10 Kondisi komponen hydraulic
34
12
11 Kondisi final drive
40
8
12 Kondisi ban
45
3
13 Kondisi steering
35
11
No.
Suara Pelanggan
14 Kondisi rem
38
9
15 Kondisi klakson
44
4
16 Kondisi wiper
48
1
17 Kondisi emergency shutdown switch
44
4
18 Kondisi Alat Pemadam Api Ringan (APAR)
37
10
19 Penangan terhadap bolt / nut yang kendur
38
9
20 Penanganan terhadap adanya oli yang bocor
47
2
21 Kenyamanan saat berkendara
47
2
22 Penggunaan bahan bakar
33
13
23 Service (PM) secara teratur
32
14
24 Respon teknisi terhadap laporan kerusakan
40
8
25 Tingkat keahlian teknisi
34
12
44
4
26
Penanggulangan terhadap kerusakan yang sering terjadi / berulang - ulang.
Based on the calculation result that can be seen in table 1, the maintenance quality aspects with the score ≥ 47 are: 1. Wiper condition 2. Panels on operator cabin condition 3. The oil leaking handling 4. The driving comfortable The next step of the HOQ design is to determine the technical language at the vertical side of House of Quality. The technical language is a planning action or activity that will be implement to improve the maintenance quality of heavy equipment at the workshop. The technical language were determined based on the data that obtained from the interviewing of workshop superintendent, recommendation from the heavy equipment dealer maintenance supervisor and some reference. This technical language also considering the eight pillars of TPM.
Penerapan model..., Deni Juharsyah, FT UI, 2009.
1.
2.
3.
4.
5.
6.
7.
8.
9.
List of the technical languages are: The Technical Analysis (TA) Technical Analysis (TA) is an inspection and measurement program to asses the pressure, temperature, cycle time and components speed. Using the original spare parts Always buy the original spare parts like filter, oil and another component only from the heavy equipment dealer. Using the quality of fuel Fuel that will used has a recommendation from the heavy equipment dealer. Daily Inspection Perform daily inspection to check the oil leaking, loosen bolt, condition of components visually, tyre pressure, greasing and oil level checking. Oil Sampling Oil sampling performed at 100 hours before the preventive maintenance execution. Coolant Radiator Sampling Coolant radiator sampling is to detect the possibility of damage on engine or cooling system. Operational Test Before operating the heavy equipment, the operator must check the condition of panels on the dashboard, gauges indicator, AC, wiper, klakson performance, operator seat, mirror, lamp, emergency shutdown switch and braking performance. Usage of Hallogen lamp The usage of Hallogen lamp is meant to make operator can see clearer at night, so that the potency of accident during working smaller. Train the maintenance staff Every six month or when buying the new aqquipment, heavy equipment dealer have to give training about procedure of heavy equipment maintenance and also introduction to the new heavy equipment operational system, especially for the main activator components.
10. Execution of PCR (Planned Component Replacement) Program. PCR program executed when the age of component reach a half of the life time usage of equipment, which is 6.000 hours.. 11. Usage of ergonomis seat Usage of ergonomis seat is intended to make operator do not be tired quickly and more comfortable when operating the heavy equipment. 12. Cleaning machine regularly Cleaning machine conducted regularly by operator shift 1 so that the risk of dirt contamination come in to the heavy equipment system and destroy heavy equipment become lower. 13. Execution of overhaul program Overhaul program is maintenance program at the time heavy equipment has entered its one life cycle that is 12.000 hours. 14. Execution of preventive maintenance Execution of PM conducted after equipment have operated for 250 hours. oli replacement, filter and repairement that have been scheduled in backlog are conducted when doing preventive maintanance. 15. Downloading Electronic Technician (ET) Electronic Technician (ET) is a software that available to record the healthy parameter of heavy equipment during its operation. 16. Improvement maintenance process Every 6 months, maintenance process that have been conducted is reviewed. This review conducted after the training that has been given by heavy equipment dealer. 17. Downloading truck payload management system (TPMS) In heavy equipment there is a software called truck payload management system (TPMS). the function of TPMS is to record burden level brought by heavy equipment, therefore we know whether it is overload or not 18. Suspension setting Suspension setting conducted every heavy equipment has operated for 1000 hours to avoid suspension damage.
Penerapan model..., Deni Juharsyah, FT UI, 2009.
19. Execution of preload bearing Preload bearing is setting conducted every 2000 hours heavy equipment operation at final drive component to avoid earlier wear caused by friction occurred at gear and others component in final drive. 20. Valve engine setting Valve engine setting conducted every 2000 hours heavy equipment operation. After obtaining technical language, the next step is determine the relationship matrix between technical language and customer desire, correlation matrix among technical language, and assess total normalization value. nilai normalisasi total. Relationship matrix is calculated to obtain the Customer Technical Interactive (CTI) value, while correlation matrix is calculated to obtain Technical Correlation Value (TCV). To get this values, the existing relationship divided in to three type that is : 1. Strong relationship (Θ) In its calculation is given by value 9. 2. Moderate relationship (Ο) In its calculation is given by value 3. 3. Weak relationship (Δ) In its calculation is given by value 1. CTI score is a measurement to know the relationship between technical language and customer desire. CTI value calculation used as follow : CTI value =
∑
n
i =1
Relationship value x
customer desire value where n : amount of customer For example, CTI value for ”good quality of fuel usage” = (9X33) + (9x33) + (1x44) = 638 In order to obtain relative weight of CTI value, the calculation used is as follow : CTIvalue
Relative weight of CTI = ∑ CTIvalue x100% For example, CTI relative weight for technical languange ”good quality of fuel usage” = (638 / 66039) x 100 % = 0,97 %
TCV value is assessment of correlation matrix among technical language. TVC calculation used is as follow : TCV value =
∑
n
i =1
Correlation value
Where n : amount of technical languange For example, TCV value for ”good quality of fuel usage” = 1 + 9 + 1 = 11 To obtain relative weight of TVC value, calculation used is as follow : TCV relative weight = Technicalcorrelationvalue x100%
∑Technicalcorrelationvalue
For example, relative weight of TCV for technical language “ good quality of fuel usage” = (11 / 724) x 100 % = 1,52 % Total normalization value is sum of relative weight of CTI and relative weight of TCV. This value will be utilized to arrange priority of technical language that will be implemented in order to fulfill customer desire. For example, total normalization value to technical language ”good quality of fuel usage” = 0,97 % + 1,52 % = 2,49 % By the same calculatiuon can be obtained CTI value, TCV and total normalization value for other technical language, as seen in table 2. Then these value are input in to the House of Quality (HoQ) as seen in Figure 2. From table 2 can be known the technical language that very influencing the attribute based on total normalization value sequences, that is : 1. Improvement of maintenance process 2. Execution of preventive maintenance 3. Train maintenance staff 4. Execute operational test 5. Execute overhaul program In order to measure efficacy of technical language implementation as a strategic decision hence in MQFD model measurement is focused at improvement of maintenance quality parameter in TPM, that is availability, Mean Time To Repair (MTTR), Mean Time Between Failure (MTBF), Mean Down Time (MDT) and Overall Equipment Effectiveness (OEE). From maintenance data processing result that has been
Penerapan model..., Deni Juharsyah, FT UI, 2009.
done previously, the parameters as seen in table 3 obtained. Table 2. Technical information value No. 1
Deskripsi Bahasa Teknis Pelaksanaan Technical Analysis (TA)
2 Penggunaan suku cadang asli
Nlai Bobot Relatif Normalisasi Total TCV
Nilai CTI
Bobot Relatif CTI
Nilai TCV
3156
20.04%
56
20.44%
40.48%
2872
18.24%
14
5.11%
23.35%
Penggunaan bahan bakar yang 3 berkualitas
638
4.05%
11
4.01%
8.07%
4 Pelaksanaan pemeriksaan harian
4746
30.14%
42
15.33%
45.47%
5 Memeriksa sampel oli
3014
19.14%
36
13.14%
32.28%
6 Memeriksa sampel coolant radiator
877
5.57%
33
12.04%
17.61%
7 Pelaksanaan test operasional
7739
49.15%
22
8.03%
57.18%
8 Pengunaan lampu hallogen
369
2.34%
0
0.00%
2.34%
9 Melatih staff pemeliharaan
5241
33.28%
58
21.17%
54.45%
10 Pelaksanaan program PCR
2736
17.38%
21
7.66%
25.04%
819
5.20%
0
0.00%
5.20%
3168
20.12%
34
12.41%
32.53%
6308
40.06%
35
12.77%
52.83%
8610
54.68%
88
32.12%
86.80%
3555
22.58%
38
13.87%
36.45%
16 Memperbaiki Proses Pemeliharaan 6278
39.87%
147
53.65%
93.52%
17 Pengunduhan TPMS
2583
16.40%
19
6.93%
23.34%
18 Penyetelan suspensi
1332
8.46%
23
8.39%
16.85%
Penggunaan tempat duduk yang ergonomis Membersihkan mesin secara 12 teratur 11
13 Pelaksanaan program overhaul Pelaksanaan pemeliharaan 14 pencegahan Pengunduhan Electronic 15 Technician (ET)
19 Pelaksanaan preload bearing
882
5.60%
20
7.30%
12.90%
20 Penyetelan valve engine
1116
7.09%
27
9.85%
16.94%
Table 3. Equipment Maintenance Performace No. Alat
Model
A11DT01 A11DT02 A11DT03 A11DT04 A11DT05 A11DT06 A11DT07 A11DT08 A11DT10 A11DT11 A11DT12 A11DT13 A11DT14 A11DT15 A11DT16
773B/D 773B/D 773B/D 773B/D 769C 769C 769C 769C 773B/D 773B/D 773B/D 777D 777D 777D 777D
% Availability 80,16 88,57 80,75 70,16 77,44 96,96 96,19 90,91 75,12 94,78 83,18 91,31 94,41 96,59 70,41
MDT (jam) 24,09 20,63 29,68 29,08 32,62 9,21 5,65 22,09 35,85 6,69 25,19 8,58 8,90 6,94 34,43
MTBF % OEE (jam) 97,30 28,91 159,81 31,95 124,52 29,13 68,37 25,31 111,96 22,82 293,91 28,57 142,89 28,35 220,93 26,79 108,25 23,60 121,51 29,78 124,59 26,13 90,08 37,07 150,32 38,33 196,81 39,22 81,91 28,58
From table 3 can be seen that each equipment has OEE value relatively lower therefore by implementing technical language which has been formulated previously, expected maintenance quality parameters can be increased. This parameters will be evaluated continuously and in this evaluation process, the
new HoQs will be made till wanted parameter value are obtained. This is the benefit of MQFD model which able to be made as continuous improvement tool and involve all the existing stake holder. 5.
Conclusion
Based on análisis resolt of maintenance parameter in TPM, known that heavy equipment performance in Workshops still need to be increased. By paying attention at voice of customer known that there are 26 attribute of customer requirement for effort of heavy equipment maintenance quality improvement in Workshops. Maintenance quality aspect which must become priority alternately is wiper condition, panels in operator cabin condition, handling to existence of leaky oil, freshment Turing driving. While technical languange which very influencing attribute base on total normalisation value alternately is improvement of maintenance process, Execution of maintenance preventive, train maintenance staff, execute operacional test, and execute overhaul program. Analysis result of MQFD model can be implemented as activity plan where its implementation in order to imprové maintenance quality and company benefits (reduction in maintenance cost) and imprové the competency of involved employer, have to be made as a priority. Reference: [1]
[2]
[3]
Pramod et al. (2006). Integrating TPM and QFD for improving quality in maintenance engineering. Journal of Quality in Maintenance Engineering, Vol. 12 No.2, p. 151. Ahmed, S., Hassan, M.H. and Taha, Z. (2005). TPM can go beyond maintenance : except from a case implementation. Journal of Quality in Maintenance Engineering, Vol. 11 No.1, p.19-42. Seth,D. and Tripathi, D. (2005). Relationship between TQM and TPM
Penerapan model..., Deni Juharsyah, FT UI, 2009.
[4]
implementation factors and business performance of manufacturing industry in Indian contrast. International Journal of Quality & Reliability Management, Vol.22 No.3, p.256-277. Fung, R.Y.K., Law, D.S.T. and Ip, W.H. (1999). Design targets determination for inter-department product attributes in QFD using fuzzy interference. Integrated
[5]
Manufacturing Systems, Vol.10 No.6, p.376-387. Zairi, M. and Youssef, M.A. (1998). Quality Function Deployment : a main pillar for successful total quality management and product development. International Journal of Quality & Reliability Management, Vol.12 No.6, p.9-23.
Figure 2. House of Quality (HoQ)
Penerapan model..., Deni Juharsyah, FT UI, 2009.
